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Metaheuristic Parameter Identification of Motors Using Dynamic Response Relations.

Omar Rodríguez-Abreo1, Juvenal Rodríguez-Reséndiz2, José Manuel Álvarez-Alvarado2

  • 1Space Robotics Laboratory, Department of Systems Engineering and Automation, Universidad de Malaga, C/Ortiz Ramos s/n, 29071 Malaga, Spain.

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PubMed
Summary

This study enhances motor model parameter estimation using metaheuristic algorithms. By incorporating steady-state and transient-state relations, the method significantly reduces computational iterations while maintaining accuracy.

Keywords:
Cuckoo SearchDC motorGrey Wolf OptimizerJayametaheuristicparameter estimation

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Area of Science:

  • Electrical Engineering
  • Computational Intelligence
  • Control Systems

Background:

  • Accurate motor model parameter estimation is crucial for system performance and control.
  • Traditional methods can be computationally intensive and may require extensive parameter tuning.
  • Metaheuristic algorithms offer a robust framework for complex optimization problems.

Purpose of the Study:

  • To improve the efficiency of metaheuristic algorithms for motor model parametric estimation.
  • To leverage steady-state and transient-state dynamic response equations within metaheuristic searches.
  • To demonstrate the applicability and benefits across various metaheuristic algorithms.

Main Methods:

  • Utilized dynamic response equations for step input to define search space constraints in metaheuristic algorithms.
  • Implemented Gray Wolf Optimizer, Jaya Algorithm, and Cuckoo Search Algorithm for parametric estimation.
  • Conducted tests on two motors with known parameters, varying algorithm parameters like iterations and population size.

Main Results:

  • The modified metaheuristic approach reduced the number of required iterations by 10% to 50% compared to original methods.
  • Achieved comparable estimation accuracy to existing techniques with fewer computational steps.
  • Demonstrated consistent performance improvements across multiple tested metaheuristic algorithms.

Conclusions:

  • Incorporating dynamic response equations effectively constrains metaheuristic searches, enhancing efficiency.
  • The proposed method offers a significant speed-up in motor model parameter estimation.
  • This approach is broadly applicable to various metaheuristic optimization techniques in electrical engineering.